class_docstr: Base class of stacking classifiers
docstr: Predict class probabilities for X.
  
  Parameters
  ----------
  X : {array-like, sparse matrix}, shape = [n_samples, n_features]
      Training vectors, where n_samples is the number of samples and
      n_features is the number of features.
  
  Returns
  ----------
  proba : array-like, shape = [n_samples, n_classes] or a list of                 n_outputs of such arrays if n_outputs > 1.
      Probability for each class per sample.
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code: def predict_proba(self, X):
      """ Predict class probabilities for X.
  
          Parameters
          ----------
          X : {array-like, sparse matrix}, shape = [n_samples, n_features]
              Training vectors, where n_samples is the number of samples and
              n_features is the number of features.
  
          Returns
          ----------
          proba : array-like, shape = [n_samples, n_classes] or a list of \
                  n_outputs of such arrays if n_outputs > 1.
              Probability for each class per sample.
  
          """
      check_is_fitted(self, ["clfs_", "meta_clf_"])
  
      return self._do_predict(X, self.meta_clf_.predict_proba)
  
file_path: mlxtend/mlxtend/classifier/_base_classification.py
imports: import numpy
  from scipy import sparse
  from sklearn.base import ClassifierMixin
  from ..externals.estimator_checks import check_is_fitted
label_desc: Use image node_id 3 for calling the _BaseStackingClassifier obj's underlying member method code with example usage: obj.predict_proba(X) and returns: self